{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Sentiment_Analysis_Twitter.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4nbn4-VN-Obv"
      },
      "source": [
        "**Twitter Sentiment Analysis**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uOlyDfkg-eEk"
      },
      "source": [
        "This notebook contains model which is able to differniate between positive and negative sentiment."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c5SpfI3d-zY5"
      },
      "source": [
        "#Importing all the required libraries\r\n",
        "import re\r\n",
        "import pandas as pd \r\n",
        "import numpy as np \r\n",
        "import matplotlib.pyplot as plt \r\n",
        "import seaborn as sns\r\n",
        "import string\r\n",
        "import nltk\r\n",
        "import warnings\r\n",
        "from sklearn.model_selection import train_test_split\r\n",
        "from sklearn.linear_model import LogisticRegression\r\n",
        "from xgboost import XGBClassifier \r\n",
        "from sklearn.metrics import f1_score\r\n",
        "from sklearn.metrics import accuracy_score\r\n",
        "from sklearn.tree import DecisionTreeClassifier\r\n",
        "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\r\n",
        "\r\n",
        "\r\n",
        "%matplotlib inline"
      ],
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "id": "EAZfhwCl_EFJ",
        "outputId": "7e851a8c-114c-43dc-e39c-82320257d354"
      },
      "source": [
        "#Loading of training dataset\r\n",
        "train=pd.read_csv('/content/train_SentimentAnalysis.csv')\r\n",
        "train.head()"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>label</th>\n",
              "      <th>tweet</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>@user when a father is dysfunctional and is s...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>@user @user thanks for #lyft credit i can't us...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>bihday your majesty</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>#model   i love u take with u all the time in ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>factsguide: society now    #motivation</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id  label                                              tweet\n",
              "0   1      0   @user when a father is dysfunctional and is s...\n",
              "1   2      0  @user @user thanks for #lyft credit i can't us...\n",
              "2   3      0                                bihday your majesty\n",
              "3   4      0  #model   i love u take with u all the time in ...\n",
              "4   5      0             factsguide: society now    #motivation"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LB3t6Pl3_bw9",
        "outputId": "9748ae42-5d69-47cf-dde0-c11cfaf4bce0"
      },
      "source": [
        "#Shape of training set\r\n",
        "print(\"Shape of Training dataset:\",train.shape)"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of Training dataset: (31962, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "id": "-tvAVtLG_vtN",
        "outputId": "fe2a9aa1-e614-479e-cfd0-d6fc1c5dd22b"
      },
      "source": [
        "#Loading of test dataset\r\n",
        "test=pd.read_csv('/content/test_SentimentAnalysis.csv')\r\n",
        "test.head()"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>tweet</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>31963</td>\n",
              "      <td>#studiolife #aislife #requires #passion #dedic...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>31964</td>\n",
              "      <td>@user #white #supremacists want everyone to s...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>31965</td>\n",
              "      <td>safe ways to heal your #acne!!    #altwaystohe...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>31966</td>\n",
              "      <td>is the hp and the cursed child book up for res...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>31967</td>\n",
              "      <td>3rd #bihday to my amazing, hilarious #nephew...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      id                                              tweet\n",
              "0  31963  #studiolife #aislife #requires #passion #dedic...\n",
              "1  31964   @user #white #supremacists want everyone to s...\n",
              "2  31965  safe ways to heal your #acne!!    #altwaystohe...\n",
              "3  31966  is the hp and the cursed child book up for res...\n",
              "4  31967    3rd #bihday to my amazing, hilarious #nephew..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5VOA8izvAxPl",
        "outputId": "e7398aeb-6914-44d2-c5e7-cfdf2fc760e0"
      },
      "source": [
        "#Shape of test dataset\r\n",
        "print(\"Shape of test dataset:\",test.shape)"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of test dataset: (17197, 2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "id": "Emeh4bdTA7H5",
        "outputId": "e252b8f2-4e36-43bc-a8ba-e89392892380"
      },
      "source": [
        "#Combining test and training dataset for preprocessing\r\n",
        "combine = train.append(test,ignore_index=True,sort=True)\r\n",
        "combine.head()"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>label</th>\n",
              "      <th>tweet</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>@user when a father is dysfunctional and is s...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>@user @user thanks for #lyft credit i can't us...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>bihday your majesty</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>#model   i love u take with u all the time in ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>factsguide: society now    #motivation</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id  label                                              tweet\n",
              "0   1    0.0   @user when a father is dysfunctional and is s...\n",
              "1   2    0.0  @user @user thanks for #lyft credit i can't us...\n",
              "2   3    0.0                                bihday your majesty\n",
              "3   4    0.0  #model   i love u take with u all the time in ...\n",
              "4   5    0.0             factsguide: society now    #motivation"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qLLqtttjBxNA"
      },
      "source": [
        "**Data Preprocessing**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QwLz28P5BMNP"
      },
      "source": [
        "#Defining function to remove user handle\r\n",
        "def remove_pattern(text,pattern):\r\n",
        "    \r\n",
        "    # re.findall() finds the pattern i.e @user and puts it in a list for further task\r\n",
        "    r = re.findall(pattern,text)\r\n",
        "    \r\n",
        "    # re.sub() removes @user from the sentences in the dataset\r\n",
        "    for i in r:\r\n",
        "        text = re.sub(i,\"\",text)\r\n",
        "    \r\n",
        "    return text"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "id": "KJ1o_32rBdKf",
        "outputId": "483d6c79-e60f-472c-d222-5cdc8af52e6f"
      },
      "source": [
        "#Removing all the user handle\r\n",
        "combine['Tidy_Tweets'] = np.vectorize(remove_pattern)(combine['tweet'], \"@[\\w]*\")\r\n",
        "\r\n",
        "combine.head()"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>label</th>\n",
              "      <th>tweet</th>\n",
              "      <th>Tidy_Tweets</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>@user when a father is dysfunctional and is s...</td>\n",
              "      <td>when a father is dysfunctional and is so sel...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>@user @user thanks for #lyft credit i can't us...</td>\n",
              "      <td>thanks for #lyft credit i can't use cause th...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>bihday your majesty</td>\n",
              "      <td>bihday your majesty</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>#model   i love u take with u all the time in ...</td>\n",
              "      <td>#model   i love u take with u all the time in ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>factsguide: society now    #motivation</td>\n",
              "      <td>factsguide: society now    #motivation</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id  ...                                        Tidy_Tweets\n",
              "0   1  ...    when a father is dysfunctional and is so sel...\n",
              "1   2  ...    thanks for #lyft credit i can't use cause th...\n",
              "2   3  ...                                bihday your majesty\n",
              "3   4  ...  #model   i love u take with u all the time in ...\n",
              "4   5  ...             factsguide: society now    #motivation\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "id": "_0wO8mqlB4SR",
        "outputId": "b4962cca-171e-43d2-92f5-4b93d8d3e5f8"
      },
      "source": [
        "#Removing all the punctuations from the tweet\r\n",
        "combine['Tidy_Tweets'] = combine['Tidy_Tweets'].str.replace(\"[^a-zA-Z#]\", \" \")\r\n",
        "\r\n",
        "combine.head()"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>label</th>\n",
              "      <th>tweet</th>\n",
              "      <th>Tidy_Tweets</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>@user when a father is dysfunctional and is s...</td>\n",
              "      <td>when a father is dysfunctional and is so sel...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>@user @user thanks for #lyft credit i can't us...</td>\n",
              "      <td>thanks for #lyft credit i can t use cause th...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>bihday your majesty</td>\n",
              "      <td>bihday your majesty</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>#model   i love u take with u all the time in ...</td>\n",
              "      <td>#model   i love u take with u all the time in ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>factsguide: society now    #motivation</td>\n",
              "      <td>factsguide  society now    #motivation</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id  ...                                        Tidy_Tweets\n",
              "0   1  ...    when a father is dysfunctional and is so sel...\n",
              "1   2  ...    thanks for #lyft credit i can t use cause th...\n",
              "2   3  ...                                bihday your majesty\n",
              "3   4  ...  #model   i love u take with u all the time in ...\n",
              "4   5  ...             factsguide  society now    #motivation\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "id": "F4xLc9QHCP5B",
        "outputId": "8c11fe78-39ad-43c6-e0ee-42abb1bdb64e"
      },
      "source": [
        "#Removing all the short words\r\n",
        "combine['Tidy_Tweets'] = combine['Tidy_Tweets'].apply(lambda x: ' '.join([w for w in x.split() if len(w)>3]))\r\n",
        "\r\n",
        "combine.head()"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>label</th>\n",
              "      <th>tweet</th>\n",
              "      <th>Tidy_Tweets</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>@user when a father is dysfunctional and is s...</td>\n",
              "      <td>when father dysfunctional selfish drags kids i...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>@user @user thanks for #lyft credit i can't us...</td>\n",
              "      <td>thanks #lyft credit cause they offer wheelchai...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>bihday your majesty</td>\n",
              "      <td>bihday your majesty</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>#model   i love u take with u all the time in ...</td>\n",
              "      <td>#model love take with time</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>factsguide: society now    #motivation</td>\n",
              "      <td>factsguide society #motivation</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id  ...                                        Tidy_Tweets\n",
              "0   1  ...  when father dysfunctional selfish drags kids i...\n",
              "1   2  ...  thanks #lyft credit cause they offer wheelchai...\n",
              "2   3  ...                                bihday your majesty\n",
              "3   4  ...                         #model love take with time\n",
              "4   5  ...                     factsguide society #motivation\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X6zjc_EBCV9p",
        "outputId": "58fa5cfd-d7a9-42c6-fd1e-e2db561537bb"
      },
      "source": [
        "#Tokenization of tweets\r\n",
        "tokenized_tweet = combine['Tidy_Tweets'].apply(lambda x: x.split())\r\n",
        "tokenized_tweet.head()"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    [when, father, dysfunctional, selfish, drags, ...\n",
              "1    [thanks, #lyft, credit, cause, they, offer, wh...\n",
              "2                              [bihday, your, majesty]\n",
              "3                     [#model, love, take, with, time]\n",
              "4                   [factsguide, society, #motivation]\n",
              "Name: Tidy_Tweets, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lGB2zJbLCejk",
        "outputId": "392cb87c-2f9c-4bed-9679-47cde8007ec3"
      },
      "source": [
        "#Stemming of the tokenized tweeets\r\n",
        "from nltk import PorterStemmer\r\n",
        "\r\n",
        "ps = PorterStemmer()\r\n",
        "\r\n",
        "tokenized_tweet = tokenized_tweet.apply(lambda x: [ps.stem(i) for i in x])\r\n",
        "\r\n",
        "tokenized_tweet.head()"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    [when, father, dysfunct, selfish, drag, kid, i...\n",
              "1    [thank, #lyft, credit, caus, they, offer, whee...\n",
              "2                              [bihday, your, majesti]\n",
              "3                     [#model, love, take, with, time]\n",
              "4                         [factsguid, societi, #motiv]\n",
              "Name: Tidy_Tweets, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "id": "lsD_uA9QC01k",
        "outputId": "009a3969-ce05-433b-a406-ec4bace3d490"
      },
      "source": [
        "for i in range(len(tokenized_tweet)):\r\n",
        "    tokenized_tweet[i] = ' '.join(tokenized_tweet[i])\r\n",
        "\r\n",
        "combine['Tidy_Tweets'] = tokenized_tweet\r\n",
        "combine.head()"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>label</th>\n",
              "      <th>tweet</th>\n",
              "      <th>Tidy_Tweets</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>@user when a father is dysfunctional and is s...</td>\n",
              "      <td>when father dysfunct selfish drag kid into dys...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>@user @user thanks for #lyft credit i can't us...</td>\n",
              "      <td>thank #lyft credit caus they offer wheelchair ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>bihday your majesty</td>\n",
              "      <td>bihday your majesti</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>#model   i love u take with u all the time in ...</td>\n",
              "      <td>#model love take with time</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>factsguide: society now    #motivation</td>\n",
              "      <td>factsguid societi #motiv</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id  ...                                        Tidy_Tweets\n",
              "0   1  ...  when father dysfunct selfish drag kid into dys...\n",
              "1   2  ...  thank #lyft credit caus they offer wheelchair ...\n",
              "2   3  ...                                bihday your majesti\n",
              "3   4  ...                         #model love take with time\n",
              "4   5  ...                           factsguid societi #motiv\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iWBfPbZnC4_7"
      },
      "source": [
        "#Importing all the required library for visualization of words\r\n",
        "from wordcloud import WordCloud,ImageColorGenerator\r\n",
        "from PIL import Image\r\n",
        "import urllib\r\n",
        "import requests"
      ],
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
        },
        "id": "C3bJM1KCDYKK",
        "outputId": "ec859c18-38fe-4366-ef5b-a31bb1dcd479"
      },
      "source": [
        "#Visualization of positive words using WordCloud\r\n",
        "all_words_positive = ' '.join(text for text in combine['Tidy_Tweets'][combine['label']==0])\r\n",
        "\r\n",
        "# combining the image with the dataset\r\n",
        "Mask = np.array(Image.open(requests.get('http://clipart-library.com/image_gallery2/Twitter-PNG-Image.png', stream=True).raw))\r\n",
        "\r\n",
        "# We use the ImageColorGenerator library from Wordcloud \r\n",
        "# Here we take the color of the image and impose it over our wordcloud\r\n",
        "image_colors = ImageColorGenerator(Mask)\r\n",
        "\r\n",
        "# Now we use the WordCloud function from the wordcloud library \r\n",
        "wc = WordCloud(background_color='black', height=1500, width=4000,mask=Mask).generate(all_words_positive)\r\n",
        "\r\n",
        "# Size of the image generated \r\n",
        "plt.figure(figsize=(10,20))\r\n",
        "\r\n",
        "# Here we recolor the words from the dataset to the image's color\r\n",
        "# recolor just recolors the default colors to the image's blue color\r\n",
        "# interpolation is used to smooth the image generated \r\n",
        "plt.imshow(wc.recolor(color_func=image_colors),interpolation=\"hamming\")\r\n",
        "\r\n",
        "plt.axis('off')\r\n",
        "plt.show()"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x1440 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
        },
        "id": "tMbRZq_3Drto",
        "outputId": "b8b0ff64-0ebe-470c-c17a-209a2c2a9ff5"
      },
      "source": [
        "#Visualiztion of negative words using WordCloud\r\n",
        "all_words_negative = ' '.join(text for text in combine['Tidy_Tweets'][combine['label']==1])\r\n",
        "\r\n",
        "# combining the image with the dataset\r\n",
        "Mask = np.array(Image.open(requests.get('http://clipart-library.com/image_gallery2/Twitter-PNG-Image.png', stream=True).raw))\r\n",
        "\r\n",
        "# We use the ImageColorGenerator library from Wordcloud \r\n",
        "# Here we take the color of the image and impose it over our wordcloud\r\n",
        "image_colors = ImageColorGenerator(Mask)\r\n",
        "\r\n",
        "# Now we use the WordCloud function from the wordcloud library \r\n",
        "wc = WordCloud(background_color='black', height=1500, width=4000,mask=Mask).generate(all_words_negative)\r\n",
        "\r\n",
        "# Size of the image generated \r\n",
        "plt.figure(figsize=(10,20))\r\n",
        "\r\n",
        "# Here we recolor the words from the dataset to the image's color\r\n",
        "# recolor just recolors the default colors to the image's blue color\r\n",
        "# interpolation is used to smooth the image generated \r\n",
        "plt.imshow(wc.recolor(color_func=image_colors),interpolation=\"gaussian\")\r\n",
        "\r\n",
        "plt.axis('off')\r\n",
        "plt.show()"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x1440 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YhiWKYTaD-Zk"
      },
      "source": [
        "#Function to extract hashtags from tweet\r\n",
        "def Hashtags_Extract(x):\r\n",
        "    hashtags=[]\r\n",
        "    \r\n",
        "    # Loop over the words in the tweet\r\n",
        "    for i in x:\r\n",
        "        ht = re.findall(r'#(\\w+)',i)\r\n",
        "        hashtags.append(ht)\r\n",
        "    \r\n",
        "    return hashtags"
      ],
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2pgAH6vfEJ1n"
      },
      "source": [
        "ht_positive = Hashtags_Extract(combine['Tidy_Tweets'][combine['label']==0])"
      ],
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZQmr4dwIEaCk"
      },
      "source": [
        "ht_positive_unnest = sum(ht_positive,[])"
      ],
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jfsIjuoiEexB"
      },
      "source": [
        "ht_negative = Hashtags_Extract(combine['Tidy_Tweets'][combine['label']==1])"
      ],
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hBgbzPqQEl4U"
      },
      "source": [
        "ht_negative_unnest = sum(ht_negative,[])"
      ],
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y6CubfTfEpcn",
        "outputId": "58177aa5-f2d5-4900-e5cb-ef73c35fc164"
      },
      "source": [
        "#Word frequency for positive words\r\n",
        "word_freq_positive = nltk.FreqDist(ht_positive_unnest)\r\n",
        "\r\n",
        "word_freq_positive"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FreqDist({'run': 72,\n",
              "          'lyft': 2,\n",
              "          'disapoint': 1,\n",
              "          'getthank': 2,\n",
              "          'model': 375,\n",
              "          'motiv': 202,\n",
              "          'allshowandnogo': 1,\n",
              "          'school': 30,\n",
              "          'exam': 9,\n",
              "          'hate': 27,\n",
              "          'imagin': 7,\n",
              "          'actorslif': 8,\n",
              "          'revolutionschool': 1,\n",
              "          'girl': 283,\n",
              "          'allin': 4,\n",
              "          'cav': 12,\n",
              "          'champion': 5,\n",
              "          'cleveland': 9,\n",
              "          'clevelandcavali': 1,\n",
              "          'ireland': 18,\n",
              "          'blog': 356,\n",
              "          'silver': 300,\n",
              "          'gold': 301,\n",
              "          'forex': 265,\n",
              "          'orlando': 239,\n",
              "          'standwithorlando': 2,\n",
              "          'pulseshoot': 11,\n",
              "          'orlandoshoot': 61,\n",
              "          'biggerproblem': 1,\n",
              "          'selfish': 3,\n",
              "          'heabreak': 19,\n",
              "          'valu': 8,\n",
              "          'love': 1654,\n",
              "          'gettingf': 1,\n",
              "          'got': 9,\n",
              "          'junior': 3,\n",
              "          'yugyoem': 1,\n",
              "          'omg': 27,\n",
              "          'thank': 534,\n",
              "          'posit': 917,\n",
              "          'friday': 237,\n",
              "          'cooki': 7,\n",
              "          'euro': 159,\n",
              "          'badday': 6,\n",
              "          'coneofsham': 1,\n",
              "          'cat': 92,\n",
              "          'piss': 11,\n",
              "          'funni': 140,\n",
              "          'laugh': 61,\n",
              "          'wine': 31,\n",
              "          'weekend': 273,\n",
              "          'tgif': 79,\n",
              "          'gamedev': 32,\n",
              "          'indiedev': 25,\n",
              "          'indiegamedev': 17,\n",
              "          'squad': 27,\n",
              "          'upsideofflorida': 7,\n",
              "          'shopalyssa': 10,\n",
              "          'smile': 676,\n",
              "          'media': 19,\n",
              "          'pressconfer': 1,\n",
              "          'antalya': 1,\n",
              "          'turkey': 14,\n",
              "          'throwback': 21,\n",
              "          'ica': 2,\n",
              "          'rip': 93,\n",
              "          'alohafriday': 4,\n",
              "          'time': 50,\n",
              "          'not': 12,\n",
              "          'exist': 4,\n",
              "          'positivevib': 30,\n",
              "          'hawaiian': 4,\n",
              "          'goodnight': 28,\n",
              "          'badmonday': 1,\n",
              "          'taylorswift': 6,\n",
              "          'travelingram': 3,\n",
              "          'dalat': 1,\n",
              "          'ripinkylif': 2,\n",
              "          'photoshop': 3,\n",
              "          'enoughisenough': 2,\n",
              "          'dontphotoshopeveryth': 1,\n",
              "          'wheresallthenaturalphoto': 1,\n",
              "          'cedarpoint': 2,\n",
              "          'bookworm': 3,\n",
              "          'ontothenextnovel': 1,\n",
              "          'flow': 6,\n",
              "          'grow': 10,\n",
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              "          'shegotmarri': 1,\n",
              "          'soccer': 9,\n",
              "          'spring': 18,\n",
              "          'season': 32,\n",
              "          'beachpark': 1,\n",
              "          'stori': 19,\n",
              "          'poet': 3,\n",
              "          'kpop': 10,\n",
              "          'essentialoil': 34,\n",
              "          'anxieti': 66,\n",
              "          'gettingold': 1,\n",
              "          'becomingbor': 1,\n",
              "          'notborderland': 1,\n",
              "          'goblizzard': 1,\n",
              "          'fatkidinacandystor': 1,\n",
              "          'young': 53,\n",
              "          'wet': 46,\n",
              "          'flower': 100,\n",
              "          'instasmil': 1,\n",
              "          'instalov': 5,\n",
              "          'msgapparelstoday': 1,\n",
              "          'lovecamp': 1,\n",
              "          'brownwood': 1,\n",
              "          'karaok': 4,\n",
              "          'friendship': 44,\n",
              "          'instacool': 17,\n",
              "          'thealter': 1,\n",
              "          'violent': 2,\n",
              "          'menmodel': 1,\n",
              "          'noooo': 1,\n",
              "          'whyyyy': 1,\n",
              "          'loveyoudesi': 1,\n",
              "          'jordan': 4,\n",
              "          'paid': 2,\n",
              "          'wear': 4,\n",
              "          'lowtop': 1,\n",
              "          'salut': 5,\n",
              "          'nyc': 43,\n",
              "          'newyorkc': 6,\n",
              "          'myview': 1,\n",
              "          'fridaynight': 7,\n",
              "          'danian': 1,\n",
              "          'colour': 16,\n",
              "          'rainbow': 14,\n",
              "          'aiel': 1,\n",
              "          'aielmaharashtra': 1,\n",
              "          'irrit': 1,\n",
              "          'rocksteddi': 1,\n",
              "          'band': 6,\n",
              "          'cheapshot': 1,\n",
              "          'lowblow': 1,\n",
              "          'parent': 24,\n",
              "          'tantrum': 1,\n",
              "          'toddler': 10,\n",
              "          'mind': 48,\n",
              "          'siri': 1,\n",
              "          'maco': 2,\n",
              "          'wwdc': 23,\n",
              "          'diy': 17,\n",
              "          'dresser': 1,\n",
              "          'nicknack': 1,\n",
              "          'newelectricscrewdriv': 1,\n",
              "          'choic': 12,\n",
              "          'alway': 11,\n",
              "          'june': 56,\n",
              "          'issu': 3,\n",
              "          'bazaarmag': 1,\n",
              "          'greec': 17,\n",
              "          'crete': 1,\n",
              "          'sabinedrift': 1,\n",
              "          'chillinn': 1,\n",
              "          'blondegirl': 1,\n",
              "          'bluedress': 3,\n",
              "          'gamereadi': 2,\n",
              "          'tahiti': 1,\n",
              "          'stupidthought': 1,\n",
              "          'lmao': 2,\n",
              "          'downasquadmemb': 1,\n",
              "          'treeout': 1,\n",
              "          'japan': 37,\n",
              "          'bbuk': 13,\n",
              "          'morn': 76,\n",
              "          'fridayfeel': 56,\n",
              "          'bristol': 5,\n",
              "          'rap': 9,\n",
              "          'hiphop': 13,\n",
              "          'comedi': 21,\n",
              "          'kanyewest': 2,\n",
              "          'merri': 2,\n",
              "          'christma': 5,\n",
              "          'new': 108,\n",
              "          'year': 11,\n",
              "          'freepik': 1,\n",
              "          'decor': 16,\n",
              "          'test': 4,\n",
              "          'tire': 46,\n",
              "          'annoy': 16,\n",
              "          'whi': 32,\n",
              "          'periscop': 16,\n",
              "          'collegebound': 2,\n",
              "          'nervou': 70,\n",
              "          'curiou': 2,\n",
              "          'bringiton': 8,\n",
              "          'lifegroup': 1,\n",
              "          'surfday': 1,\n",
              "          'novyspo': 1,\n",
              "          'novytowel': 1,\n",
              "          'surfgear': 1,\n",
              "          'showyourjeepday': 1,\n",
              "          'jeepmafia': 4,\n",
              "          'jeeplif': 5,\n",
              "          'jeep': 3,\n",
              "          'gratitud': 31,\n",
              "          'laurasworld': 2,\n",
              "          'uot': 1,\n",
              "          'savechar': 1,\n",
              "          'tragic': 28,\n",
              "          'space': 8,\n",
              "          'fabricsourc': 1,\n",
              "          'finish': 7,\n",
              "          'pillow': 2,\n",
              "          'cushion': 2,\n",
              "          'financi': 2,\n",
              "          'futur': 31,\n",
              "          'save': 16,\n",
              "          'insur': 6,\n",
              "          'invest': 6,\n",
              "          'saturday': 94,\n",
              "          'buxton': 2,\n",
              "          'grumpi': 9,\n",
              "          'troopingthecolour': 3,\n",
              "          'thequeenat': 1,\n",
              "          'fusiona': 1,\n",
              "          'develop': 4,\n",
              "          'chase': 2,\n",
              "          'hamster': 2,\n",
              "          'imissy': 3,\n",
              "          'boyfriend': 36,\n",
              "          'icr': 1,\n",
              "          'lichfield': 4,\n",
              "          'surpris': 12,\n",
              "          'unexpect': 4,\n",
              "          'embarrass': 2,\n",
              "          'internationaldayofyoga': 1,\n",
              "          'yogalov': 3,\n",
              "          'yoga': 39,\n",
              "          'conc': 20,\n",
              "          'hampden': 1,\n",
              "          'fave': 5,\n",
              "          'coupl': 50,\n",
              "          'flagday': 33,\n",
              "          'texa': 9,\n",
              "          'householdnam': 1,\n",
              "          'takingov': 1,\n",
              "          'furri': 4,\n",
              "          'persiancat': 1,\n",
              "          'kiss': 16,\n",
              "          'fridayfun': 6,\n",
              "          'theriveourberlin': 1,\n",
              "          'theboss': 2,\n",
              "          'nowplay': 18,\n",
              "          'sick': 33,\n",
              "          'littl': 18,\n",
              "          'set': 4,\n",
              "          'boohiss': 1,\n",
              "          'whenrealtorscompeteyouwin': 1,\n",
              "          'callm': 1,\n",
              "          'gotheextramil': 1,\n",
              "          'poaventura': 1,\n",
              "          'whatajok': 3,\n",
              "          'dcvote': 1,\n",
              "          'elect': 6,\n",
              "          'smh': 22,\n",
              "          'vine': 17,\n",
              "          'magic': 19,\n",
              "          'subscrib': 2,\n",
              "          'youtub': 53,\n",
              "          'intellicr': 2,\n",
              "          'pwcproud': 1,\n",
              "          'tbt': 135,\n",
              "          'puls': 24,\n",
              "          'orlandostaystrong': 1,\n",
              "          'thoughtsandpray': 3,\n",
              "          'yrkkh': 2,\n",
              "          'uglyspat': 1,\n",
              "          'thekil': 3,\n",
              "          'tim': 1,\n",
              "          'mylif': 14,\n",
              "          'russian': 4,\n",
              "          'tuesday': 33,\n",
              "          'jacki': 2,\n",
              "          'taketim': 1,\n",
              "          'soul': 13,\n",
              "          'enjoylif': 10,\n",
              "          'believ': 22,\n",
              "          'dream': 69,\n",
              "          'havefun': 5,\n",
              "          'edinburgh': 14,\n",
              "          'scotland': 14,\n",
              "          'homesweethom': 7,\n",
              "          'studiotim': 2,\n",
              "          'cfisdrrr': 1,\n",
              "          'prettypussi': 1,\n",
              "          'sexybbw': 1,\n",
              "          'realmvp': 1,\n",
              "          'excit': 23,\n",
              "          'stone': 2,\n",
              "          'gift': 32,\n",
              "          'lfc': 5,\n",
              "          'ynwa': 4,\n",
              "          'englishpremierleagu': 1,\n",
              "          'rogergoodel': 2,\n",
              "          'goodellsuck': 2,\n",
              "          'orb': 1,\n",
              "          'lovey': 18,\n",
              "          'daddi': 32,\n",
              "          'actor': 34,\n",
              "          'seeklearn': 8,\n",
              "          'stafresh': 8,\n",
              "          'bastard': 3,\n",
              "          'socialmedia': 15,\n",
              "          'smaphon': 6,\n",
              "          'lifeisbeauti': 5,\n",
              "          'loveit': 36,\n",
              "          'workhard': 5,\n",
              "          'trainhard': 6,\n",
              "          'fitlif': 2,\n",
              "          'spo': 38,\n",
              "          'dure': 4,\n",
              "          'lui': 4,\n",
              "          'oscar': 4,\n",
              "          'njoyhit': 1,\n",
              "          'nice': 48,\n",
              "          'personaltrain': 5,\n",
              "          'melancholi': 40,\n",
              "          'melancholymus': 37,\n",
              "          'tvk': 3,\n",
              "          'sinkthepink': 4,\n",
              "          'instagay': 13,\n",
              "          'celebspot': 4,\n",
              "          'ccmf': 2,\n",
              "          'mychurch': 1,\n",
              "          'frontrow': 1,\n",
              "          'upshow': 1,\n",
              "          'africa': 7,\n",
              "          'horrif': 2,\n",
              "          'unforgiv': 1,\n",
              "          'instatravel': 7,\n",
              "          'instamo': 11,\n",
              "          'igdaili': 4,\n",
              "          'instagramhub': 2,\n",
              "          'girliguessimwithh': 1,\n",
              "          'mobil': 8,\n",
              "          'whitepap': 1,\n",
              "          'rochest': 1,\n",
              "          'tattoosleev': 8,\n",
              "          'tell': 2,\n",
              "          'complain': 3,\n",
              "          'philosophi': 2,\n",
              "          'proverb': 8,\n",
              "          'lifecoach': 13,\n",
              "          'reinventimposs': 8,\n",
              "          'outsid': 4,\n",
              "          'thepeel': 1,\n",
              "          'homophob': 3,\n",
              "          'taxi': 1,\n",
              "          'driver': 3,\n",
              "          'gay': 65,\n",
              "          'melbourn': 7,\n",
              "          'kind': 14,\n",
              "          'window': 11,\n",
              "          'browser': 5,\n",
              "          'customcasetab': 1,\n",
              "          'noedit': 1,\n",
              "          'totalpolitician': 1,\n",
              "          'outoftouch': 2,\n",
              "          'truth': 57,\n",
              "          'suicid': 28,\n",
              "          'blackboy': 1,\n",
              "          'istanbul': 8,\n",
              "          'swing': 2,\n",
              "          'mybabygirl': 2,\n",
              "          'babi': 109,\n",
              "          'littlegirl': 1,\n",
              "          'park': 16,\n",
              "          'winter': 17,\n",
              "          'bathtim': 2,\n",
              "          'cozi': 1,\n",
              "          'sunflow': 4,\n",
              "          'natur': 91,\n",
              "          'photograpi': 4,\n",
              "          'growth': 11,\n",
              "          'hack': 7,\n",
              "          'confer': 6,\n",
              "          'growthwithhubspot': 4,\n",
              "          'dogsoftwitt': 9,\n",
              "          'sleepnumb': 1,\n",
              "          'bonustolongrest': 1,\n",
              "          'dnd': 1,\n",
              "          'satisfi': 12,\n",
              "          'workit': 1,\n",
              "          'longhair': 12,\n",
              "          'selfporait': 6,\n",
              "          'golinuntern': 2,\n",
              "          ...})"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "id": "TyUPrQWPE7W6",
        "outputId": "41dbb228-d9f9-430e-aac5-7757ac4d6c30"
      },
      "source": [
        "#Creating dataframe for most used positive words\r\n",
        "df_positive = pd.DataFrame({'Hashtags':list(word_freq_positive.keys()),'Count':list(word_freq_positive.values())})\r\n",
        "\r\n",
        "df_positive.head()"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Hashtags</th>\n",
              "      <th>Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>run</td>\n",
              "      <td>72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>lyft</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>disapoint</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>getthank</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>model</td>\n",
              "      <td>375</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Hashtags  Count\n",
              "0        run     72\n",
              "1       lyft      2\n",
              "2  disapoint      1\n",
              "3   getthank      2\n",
              "4      model    375"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "IMUsa5eQFZUO",
        "outputId": "be0a5d5f-3c1b-4aad-ab14-30cc07bf4fe6"
      },
      "source": [
        "#Bar plot for 10 most used positive words\r\n",
        "df_positive_plot = df_positive.nlargest(20,columns='Count') \r\n",
        "sns.barplot(data=df_positive_plot,y='Hashtags',x='Count')\r\n",
        "sns.despine()"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3_igw1wCFkX1",
        "outputId": "1250318e-8462-40cf-ab34-ed1aea42c08b"
      },
      "source": [
        "#Counting the frequency for 10 most used negative words\r\n",
        "word_freq_negative = nltk.FreqDist(ht_negative_unnest)\r\n",
        "\r\n",
        "word_freq_negative"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FreqDist({'cnn': 10,\n",
              "          'michigan': 2,\n",
              "          'tcot': 14,\n",
              "          'australia': 6,\n",
              "          'opkillingbay': 5,\n",
              "          'seashepherd': 22,\n",
              "          'helpcovedolphin': 3,\n",
              "          'thecov': 4,\n",
              "          'neverump': 8,\n",
              "          'xenophobia': 12,\n",
              "          'love': 11,\n",
              "          'peac': 8,\n",
              "          'race': 14,\n",
              "          'ident': 1,\n",
              "          'med': 1,\n",
              "          'altright': 18,\n",
              "          'whitesupremaci': 6,\n",
              "          'linguist': 1,\n",
              "          'power': 2,\n",
              "          'raciolinguist': 1,\n",
              "          'brexit': 27,\n",
              "          'peopl': 11,\n",
              "          'trump': 136,\n",
              "          'republican': 13,\n",
              "          'michelleobama': 8,\n",
              "          'knick': 1,\n",
              "          'golf': 1,\n",
              "          'jewishsupremacist': 1,\n",
              "          'libtard': 77,\n",
              "          'sjw': 75,\n",
              "          'liber': 81,\n",
              "          'polit': 95,\n",
              "          'trash': 1,\n",
              "          'hate': 37,\n",
              "          'stereotyp': 4,\n",
              "          'prejudic': 3,\n",
              "          'hope': 5,\n",
              "          'conflict': 1,\n",
              "          'pol': 1,\n",
              "          'bluelivesmatt': 3,\n",
              "          'draintheswamp': 3,\n",
              "          'ferguson': 2,\n",
              "          'antisemit': 12,\n",
              "          'hocoschool': 2,\n",
              "          'columbiamd': 2,\n",
              "          'hocomd': 2,\n",
              "          'nazi': 16,\n",
              "          'hatr': 18,\n",
              "          'bigotri': 21,\n",
              "          'fyi': 2,\n",
              "          'topoli': 1,\n",
              "          'blacklivesmatt': 21,\n",
              "          'ushistori': 2,\n",
              "          'eugen': 3,\n",
              "          'biher': 5,\n",
              "          'potu': 5,\n",
              "          'theresist': 12,\n",
              "          'crime': 2,\n",
              "          'offic': 1,\n",
              "          'black': 46,\n",
              "          'retweet': 63,\n",
              "          'tampa': 32,\n",
              "          'miami': 46,\n",
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              "          'hillari': 7,\n",
              "          'misandri': 1,\n",
              "          'dumb': 2,\n",
              "          'pelosi': 1,\n",
              "          'kindled': 2,\n",
              "          'bookpublish': 1,\n",
              "          'publish': 1,\n",
              "          'kindl': 2,\n",
              "          'microsoft': 1,\n",
              "          'twitterbot': 1,\n",
              "          'tech': 3,\n",
              "          'africa': 2,\n",
              "          'po': 3,\n",
              "          'socialmooc': 1,\n",
              "          'feliz': 1,\n",
              "          'amwrit': 3,\n",
              "          'raci': 3,\n",
              "          'tlot': 4,\n",
              "          'microaggress': 2,\n",
              "          'human': 1,\n",
              "          'cruel': 1,\n",
              "          'bitter': 2,\n",
              "          'sociopath': 3,\n",
              "          'fugli': 1,\n",
              "          'lunat': 2,\n",
              "          'presidentpussygrabb': 1,\n",
              "          'tinyhand': 1,\n",
              "          'putinpuppet': 1,\n",
              "          'unless': 1,\n",
              "          'garbag': 1,\n",
              "          'man': 1,\n",
              "          'hillbilli': 1,\n",
              "          'nation': 2,\n",
              "          'fascist': 10,\n",
              "          'ukrain': 1,\n",
              "          'volodymyrviatrovych': 1,\n",
              "          'whitewash': 1,\n",
              "          'manhattan': 1,\n",
              "          'rel': 1,\n",
              "          'moron': 4,\n",
              "          'hillarylost': 1,\n",
              "          'denzelwashington': 1,\n",
              "          'morganfreeman': 2,\n",
              "          'willsmith': 1,\n",
              "          'bestof': 1,\n",
              "          'marriag': 1,\n",
              "          'mtv': 4,\n",
              "          'ccot': 2,\n",
              "          'confeder': 1,\n",
              "          'southern': 1,\n",
              "          'stloui': 2,\n",
              "          'stl': 2,\n",
              "          'obamacar': 1,\n",
              "          'energi': 1,\n",
              "          'twitter': 2,\n",
              "          'radic': 1,\n",
              "          'indoctrin': 1,\n",
              "          'rac': 2,\n",
              "          'quot': 2,\n",
              "          'kanyewest': 1,\n",
              "          'racewarchess': 1,\n",
              "          'muslimbrotherhood': 1,\n",
              "          'putinsbitch': 1,\n",
              "          'good': 1,\n",
              "          'bolt': 1,\n",
              "          'fuckdonaldtrump': 1,\n",
              "          'presid': 1,\n",
              "          'slaveown': 1,\n",
              "          'fire': 1,\n",
              "          'ufc': 2,\n",
              "          'rondarousey': 3,\n",
              "          'sad': 1,\n",
              "          'sorelos': 1,\n",
              "          'triggerwarn': 1,\n",
              "          'fathersday': 1,\n",
              "          'sistersofcolour': 1,\n",
              "          'celebritydeath': 1,\n",
              "          'woman': 2,\n",
              "          'pornisforlos': 1,\n",
              "          'govegan': 1,\n",
              "          'notrump': 1,\n",
              "          'nokkk': 1,\n",
              "          'starbuck': 1,\n",
              "          'divis': 1,\n",
              "          'nationalist': 1,\n",
              "          'crazi': 2,\n",
              "          'insan': 2,\n",
              "          'kwanzaa': 2,\n",
              "          'nia': 1,\n",
              "          'carriefish': 1,\n",
              "          'ageism': 2,\n",
              "          'hollywood': 5,\n",
              "          'pen': 1,\n",
              "          'act': 1,\n",
              "          'talent': 1,\n",
              "          'whiteman': 1,\n",
              "          'catalunya': 1,\n",
              "          'socialmedia': 2,\n",
              "          'islamicrad': 1,\n",
              "          'islamopho': 1,\n",
              "          'wypipo': 3,\n",
              "          'slaver': 2,\n",
              "          'santaproject': 1,\n",
              "          'ivanka': 1,\n",
              "          'maci': 1,\n",
              "          'jarodtaylor': 1,\n",
              "          'asslick': 1,\n",
              "          'australian': 2,\n",
              "          'shabbatshalom': 1,\n",
              "          'stalk': 2,\n",
              "          'gaslight': 3,\n",
              "          'threat': 2,\n",
              "          'suicid': 2,\n",
              "          'nsfw': 2,\n",
              "          'self': 2,\n",
              "          'notmypresident': 1,\n",
              "          'theresista': 1,\n",
              "          'drsuess': 1,\n",
              "          'moonman': 1,\n",
              "          'offens': 2,\n",
              "          'funni': 1,\n",
              "          'comedi': 2,\n",
              "          'lol': 3,\n",
              "          'edgi': 1,\n",
              "          'dankmem': 1,\n",
              "          'meme': 4,\n",
              "          'face': 1,\n",
              "          'tweet': 4,\n",
              "          'thekingsspeech': 1,\n",
              "          'shitler': 1,\n",
              "          'emirat': 1,\n",
              "          'whitefish': 2,\n",
              "          'montana': 2,\n",
              "          'whitefragil': 1,\n",
              "          'canada': 3,\n",
              "          'fatti': 1,\n",
              "          'voterid': 2,\n",
              "          'dnc': 1,\n",
              "          'vote': 1,\n",
              "          'fauxnew': 1,\n",
              "          'religion': 5,\n",
              "          'leftistlunat': 1,\n",
              "          'everydayhero': 1,\n",
              "          'done': 1,\n",
              "          'delta': 3,\n",
              "          'airlin': 1,\n",
              "          'rude': 2,\n",
              "          'comment': 2,\n",
              "          'action': 1,\n",
              "          'neverdelta': 1,\n",
              "          'jetlag': 1,\n",
              "          'genderstereotyp': 1,\n",
              "          'netanyahuspeech': 1,\n",
              "          'islamaphobia': 2,\n",
              "          'justjustic': 1,\n",
              "          'unfittobeinpublicoffic': 1,\n",
              "          'ugli': 1,\n",
              "          'reprehens': 1,\n",
              "          'supermariorun': 1,\n",
              "          'firstworldproblem': 1,\n",
              "          'womensordin': 1,\n",
              "          'democr': 1,\n",
              "          'countri': 1,\n",
              "          'qatar': 1,\n",
              "          'breitba': 1,\n",
              "          'merrychristma': 2,\n",
              "          'highereduc': 1,\n",
              "          'waitingf': 2,\n",
              "          'wildest': 2,\n",
              "          'cutest': 2,\n",
              "          'takuyakimura': 2,\n",
              "          'highfashion': 2,\n",
              "          'brand': 2,\n",
              "          'bookreview': 1,\n",
              "          'horror': 1,\n",
              "          'mattruff': 1,\n",
              "          'rout': 2,\n",
              "          'segreg': 2,\n",
              "          'cabl': 1,\n",
              "          'disney': 1,\n",
              "          'cameronstaff': 2,\n",
              "          'camstaff': 2,\n",
              "          'officerstaff': 2,\n",
              "          'acab': 3,\n",
              "          'cowardcop': 2,\n",
              "          'georgia': 2,\n",
              "          'asia': 1,\n",
              "          'hoodiesup': 1,\n",
              "          'despit': 1,\n",
              "          'demagogueri': 1,\n",
              "          'obstruct': 1,\n",
              "          'narcissist': 3,\n",
              "          'stopsex': 1,\n",
              "          'womensright': 1,\n",
              "          'christma': 4,\n",
              "          'narcissim': 1,\n",
              "          'toler': 1,\n",
              "          'all': 1,\n",
              "          'blacksagainsttrump': 1,\n",
              "          'cjreform': 1,\n",
              "          'deathpenalti': 1,\n",
              "          'juvenilejustic': 1,\n",
              "          'bailreform': 1,\n",
              "          'prosecutorialmisconduct': 1,\n",
              "          'expat': 3,\n",
              "          'classism': 3,\n",
              "          'imperialsm': 2,\n",
              "          'loser': 3,\n",
              "          'thefutur': 1,\n",
              "          'nofear': 1,\n",
              "          'inferioritycomplex': 1,\n",
              "          'fabul': 1,\n",
              "          'smug': 1,\n",
              "          'annihil': 1,\n",
              "          'voter': 1,\n",
              "          'video': 2,\n",
              "          'weirdo': 1,\n",
              "          'mental': 1,\n",
              "          'intrumpsamerica': 1,\n",
              "          'egomaniac': 1,\n",
              "          'selfi': 3,\n",
              "          'messi': 3,\n",
              "          'mecca': 3,\n",
              "          'shame': 2,\n",
              "          'public': 1,\n",
              "          'taharrush': 2,\n",
              "          'taharrushgamea': 2,\n",
              "          'rapeuge': 2,\n",
              "          'blacktwitt': 4,\n",
              "          'bcot': 1,\n",
              "          'afghanistan': 1,\n",
              "          'mannequinchalleng': 1,\n",
              "          'ceo': 1,\n",
              "          'obaman': 1,\n",
              "          'tori': 3,\n",
              "          'capitalist': 1,\n",
              "          'labour': 1,\n",
              "          'socialist': 1,\n",
              "          'green': 1,\n",
              "          'environmentalist': 1,\n",
              "          'coalitionist': 1,\n",
              "          'ukip': 2,\n",
              "          'kickitout': 1,\n",
              "          'shelvey': 1,\n",
              "          'powerhungrytraitor': 2,\n",
              "          'hereticfound': 2,\n",
              "          'nationdivid': 1,\n",
              "          'ghostbust': 1,\n",
              "          'whiner': 1,\n",
              "          'scrub': 1,\n",
              "          'stillwithh': 3,\n",
              "          'wakeup': 1,\n",
              "          'nohat': 1,\n",
              "          'solidar': 1,\n",
              "          'onelov': 1,\n",
              "          'paid': 1,\n",
              "          'fabric': 1,\n",
              "          'stori': 1,\n",
              "          'narr': 2,\n",
              "          'cancel': 1,\n",
              "          'unitedn': 1,\n",
              "          'zionism': 5,\n",
              "          'saturdayblogshar': 1,\n",
              "          'hillbot': 2,\n",
              "          'plannedparenthood': 1,\n",
              "          'ideolog': 2,\n",
              "          'stupid': 5,\n",
              "          'russiag': 1,\n",
              "          'votersuppress': 1,\n",
              "          'tyranni': 2,\n",
              "          'sexualpred': 5,\n",
              "          'beforesex': 1,\n",
              "          'cantanker': 1,\n",
              "          'administr': 1,\n",
              "          'emmaston': 1,\n",
              "          'teamd': 1,\n",
              "          'rape': 7,\n",
              "          'freedom': 2,\n",
              "          'ebook': 1,\n",
              "          'book': 1,\n",
              "          'dick': 1,\n",
              "          'childlabor': 1,\n",
              "          'politicalcorrect': 1,\n",
              "          'spoti': 1,\n",
              "          'murrayhat': 1,\n",
              "          'murray': 1,\n",
              "          'gazza': 1,\n",
              "          'drunk': 1,\n",
              "          'wifebeat': 1,\n",
              "          'whitegenocid': 5,\n",
              "          'patriarchi': 2,\n",
              "          'gegenrecht': 1,\n",
              "          'nostrach': 1,\n",
              "          'noafd': 1,\n",
              "          'nopegida': 1,\n",
              "          'popul': 1,\n",
              "          'nonazi': 1,\n",
              "          'aufstehn': 1,\n",
              "          'adl': 1,\n",
              "          'jewworldord': 1,\n",
              "          'korean': 5,\n",
              "          'anaco': 1,\n",
              "          'icon': 1,\n",
              "          'lovebeingalegend': 1,\n",
              "          'tefugeeswelcom': 1,\n",
              "          'aicl': 1,\n",
              "          'realitycheck': 2,\n",
              "          'socialjustic': 3,\n",
              "          'lithuania': 1,\n",
              "          'followm': 1,\n",
              "          'blog': 1,\n",
              "          'trumpleak': 1,\n",
              "          'misogynisttrump': 1,\n",
              "          'aberdeen': 1,\n",
              "          'mediamisogyni': 1,\n",
              "          'hanukkah': 1,\n",
              "          'andrewanglin': 1,\n",
              "          'tyruswong': 1,\n",
              "          'bambi': 1,\n",
              "          'teapay': 2,\n",
              "          'share': 1,\n",
              "          'lockerroomtalk': 1,\n",
              "          'youdisgustm': 1,\n",
              "          'kickrock': 1,\n",
              "          'cnv': 1,\n",
              "          'povey': 6,\n",
              "          'climat': 1,\n",
              "          'amerikkka': 2,\n",
              "          'disgrac': 2,\n",
              "          'save': 1,\n",
              "          'thepoliticalmillenni': 1,\n",
              "          'class': 2,\n",
              "          'doublestandard': 3,\n",
              "          'drug': 1,\n",
              "          'coloni': 4,\n",
              "          'tie': 1,\n",
              "          'market': 2,\n",
              "          ...})"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "id": "ORxZS1HsGfYg",
        "outputId": "284091b0-3c31-43f3-c054-d6f4128688ed"
      },
      "source": [
        "#Creating dataframe for most used negative words\r\n",
        "df_negative = pd.DataFrame({'Hashtags':list(word_freq_negative.keys()),'Count':list(word_freq_negative.values())})\r\n",
        "\r\n",
        "df_negative.head()"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Hashtags</th>\n",
              "      <th>Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>cnn</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>michigan</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tcot</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>australia</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>opkillingbay</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Hashtags  Count\n",
              "0           cnn     10\n",
              "1      michigan      2\n",
              "2          tcot     14\n",
              "3     australia      6\n",
              "4  opkillingbay      5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "-SMPDOUhG0sd",
        "outputId": "f0b5d3af-05da-4fae-cf06-15652c2a57af"
      },
      "source": [
        "#Plotting graph for 10 most used negative words\r\n",
        "df_negative_plot = df_negative.nlargest(20,columns='Count') \r\n",
        "sns.barplot(data=df_negative_plot,y='Hashtags',x='Count')\r\n",
        "sns.despine()"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "id": "wWkuYVcJHKng",
        "outputId": "4b7e6f60-0e01-4f1f-a677-c1a50a051e82"
      },
      "source": [
        "#Extracting features from cleaned tweets\r\n",
        "from sklearn.feature_extraction.text import CountVectorizer\r\n",
        "\r\n",
        "bow_vectorizer = CountVectorizer(max_df=0.90, min_df=2, max_features=1000, stop_words='english')\r\n",
        "\r\n",
        "# bag-of-words feature matrix\r\n",
        "bow = bow_vectorizer.fit_transform(combine['Tidy_Tweets'])\r\n",
        "\r\n",
        "df_bow = pd.DataFrame(bow.todense())\r\n",
        "\r\n",
        "df_bow"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th>36</th>\n",
              "      <th>37</th>\n",
              "      <th>38</th>\n",
              "      <th>39</th>\n",
              "      <th>...</th>\n",
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              "      <th>978</th>\n",
              "      <th>979</th>\n",
              "      <th>980</th>\n",
              "      <th>981</th>\n",
              "      <th>982</th>\n",
              "      <th>983</th>\n",
              "      <th>984</th>\n",
              "      <th>985</th>\n",
              "      <th>986</th>\n",
              "      <th>987</th>\n",
              "      <th>988</th>\n",
              "      <th>989</th>\n",
              "      <th>990</th>\n",
              "      <th>991</th>\n",
              "      <th>992</th>\n",
              "      <th>993</th>\n",
              "      <th>994</th>\n",
              "      <th>995</th>\n",
              "      <th>996</th>\n",
              "      <th>997</th>\n",
              "      <th>998</th>\n",
              "      <th>999</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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            ],
            "text/plain": [
              "       0    1    2    3    4    5    6    ...  993  994  995  996  997  998  999\n",
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              "...    ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...\n",
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              "\n",
              "[49159 rows x 1000 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "id": "zxLWt3OPSPWz",
        "outputId": "0ca99386-a91e-4963-9836-2c75c9d2821d"
      },
      "source": [
        "#Extracting from the TF-IDF features\r\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\r\n",
        "\r\n",
        "tfidf=TfidfVectorizer(max_df=0.90, min_df=2,max_features=1000,stop_words='english')\r\n",
        "\r\n",
        "tfidf_matrix=tfidf.fit_transform(combine['Tidy_Tweets'])\r\n",
        "\r\n",
        "df_tfidf = pd.DataFrame(tfidf_matrix.todense())\r\n",
        "\r\n",
        "df_tfidf"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49154</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "<p>49159 rows × 1000 columns</p>\n",
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            ],
            "text/plain": [
              "       0    1    2    3    4    5    6    ...  993  994  995  996  997  998  999\n",
              "0      0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
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              "\n",
              "[49159 rows x 1000 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bsTidAIESx4z"
      },
      "source": [
        "**Applying Various Machine Learning Models**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A3TeTDnbS4C5",
        "outputId": "9ec70a9e-0777-4b46-f3c8-60e2506c9ccf"
      },
      "source": [
        "train_bow = bow[:31962]\r\n",
        "\r\n",
        "train_bow.todense()"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "matrix([[0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        ...,\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0],\n",
              "        [0, 0, 0, ..., 0, 0, 0]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D9y3n_ImUQbC",
        "outputId": "2af1b864-0448-43cd-9fc9-49d9cd790050"
      },
      "source": [
        "train_tfidf_matrix = tfidf_matrix[:31962]\r\n",
        "\r\n",
        "train_tfidf_matrix.todense()"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "matrix([[0., 0., 0., ..., 0., 0., 0.],\n",
              "        [0., 0., 0., ..., 0., 0., 0.],\n",
              "        [0., 0., 0., ..., 0., 0., 0.],\n",
              "        ...,\n",
              "        [0., 0., 0., ..., 0., 0., 0.],\n",
              "        [0., 0., 0., ..., 0., 0., 0.],\n",
              "        [0., 0., 0., ..., 0., 0., 0.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fq-6HU96UVT2"
      },
      "source": [
        "#Spliting into training and testing data(Bag of words)\r\n",
        "x_train_bow,x_valid_bow,y_train_bow,y_valid_bow = train_test_split(train_bow,train['label'],test_size=0.3,random_state=2)"
      ],
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jfB9hblXU2u8"
      },
      "source": [
        "#Spliting into training and testing data(TF-IDF)\r\n",
        "x_train_tfidf,x_valid_tfidf,y_train_tfidf,y_valid_tfidf = train_test_split(train_tfidf_matrix,train['label'],test_size=0.3,random_state=17)"
      ],
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YXTcyzI6VSrF",
        "outputId": "a59afc3b-47e1-4928-ebc3-2617e8f63e47"
      },
      "source": [
        "#Applying Logistic Regression model on the given data set\r\n",
        "Log_Reg = LogisticRegression(random_state=0,solver='lbfgs')\r\n",
        "\r\n",
        "# Fitting the Logistic Regression Model\r\n",
        "\r\n",
        "Log_Reg.fit(x_train_bow,y_train_bow)"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=0, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d35dV7k8V7b_",
        "outputId": "8e7dd05c-2447-4831-a85c-e5c1c7bbfa4f"
      },
      "source": [
        "# The first part of the list is predicting probabilities for label:0 \r\n",
        "# and the second part of the list is predicting probabilities for label:1\r\n",
        "prediction_bow = Log_Reg.predict_proba(x_valid_bow)\r\n",
        "\r\n",
        "prediction_bow"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[9.86501156e-01, 1.34988440e-02],\n",
              "       [9.99599096e-01, 4.00904144e-04],\n",
              "       [9.13577383e-01, 8.64226167e-02],\n",
              "       ...,\n",
              "       [8.95457155e-01, 1.04542845e-01],\n",
              "       [9.59736065e-01, 4.02639345e-02],\n",
              "       [9.67541420e-01, 3.24585797e-02]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C3otaEmcV-vF",
        "outputId": "13c5c6ff-e390-4108-b56c-f6475d92b4c3"
      },
      "source": [
        "# if prediction is greater than or equal to 0.3 than 1 else 0\r\n",
        "# Where 0 is for positive sentiment tweets and 1 for negative sentiment tweets\r\n",
        "prediction_int = prediction_bow[:,1]>=0.3\r\n",
        "\r\n",
        "prediction_int = prediction_int.astype(np.int)\r\n",
        "prediction_int\r\n",
        "\r\n",
        "# calculating f1 score\r\n",
        "log_bow = f1_score(y_valid_bow, prediction_int)\r\n",
        "\r\n",
        "log_bow"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5721352019785655"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zOMJv0A7WNhD",
        "outputId": "e8f3a48a-04f4-4317-9752-0ea3c0bd06b2"
      },
      "source": [
        "#Using TF-IDF features\r\n",
        "Log_Reg.fit(x_train_tfidf,y_train_tfidf)"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=0, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yo_dKnxqWTZU",
        "outputId": "0b06cf9b-d54f-4ec8-e298-56fc5f4d78e0"
      },
      "source": [
        "prediction_tfidf = Log_Reg.predict_proba(x_valid_tfidf)\r\n",
        "\r\n",
        "prediction_tfidf"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.98487907, 0.01512093],\n",
              "       [0.97949889, 0.02050111],\n",
              "       [0.9419737 , 0.0580263 ],\n",
              "       ...,\n",
              "       [0.98630906, 0.01369094],\n",
              "       [0.96746188, 0.03253812],\n",
              "       [0.99055287, 0.00944713]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tp6WoVl7Wn3e",
        "outputId": "687a39e3-652a-453e-ffca-dabb5ba2720f"
      },
      "source": [
        "#Calculating f1 score\r\n",
        "prediction_int = prediction_tfidf[:,1]>=0.3\r\n",
        "\r\n",
        "prediction_int = prediction_int.astype(np.int)\r\n",
        "prediction_int\r\n",
        "\r\n",
        "# calculating f1 score\r\n",
        "log_tfidf = f1_score(y_valid_tfidf, prediction_int)\r\n",
        "\r\n",
        "log_tfidf"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5862068965517241"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "69wBrhqAW1Xb",
        "outputId": "40a1bdff-9a07-479e-8a04-b333cdf6a736"
      },
      "source": [
        "#Using XGBoost classifier\r\n",
        "model_bow = XGBClassifier(random_state=22,learning_rate=0.9)\r\n",
        "model_bow.fit(x_train_bow, y_train_bow)"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
              "              colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
              "              learning_rate=0.9, max_delta_step=0, max_depth=3,\n",
              "              min_child_weight=1, missing=None, n_estimators=100, n_jobs=1,\n",
              "              nthread=None, objective='binary:logistic', random_state=22,\n",
              "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
              "              silent=None, subsample=1, verbosity=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JZebgngoXLFx",
        "outputId": "361f6eae-951b-410c-c758-8cc212f54ef1"
      },
      "source": [
        "# The first part of the list is predicting probabilities for label:0 \r\n",
        "# and the second part of the list is predicting probabilities for label:1\r\n",
        "xgb=model_bow.predict_proba(x_valid_bow)\r\n",
        "\r\n",
        "xgb"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.9717447 , 0.02825526],\n",
              "       [0.9976769 , 0.00232312],\n",
              "       [0.9436968 , 0.0563032 ],\n",
              "       ...,\n",
              "       [0.9660848 , 0.03391521],\n",
              "       [0.9436968 , 0.0563032 ],\n",
              "       [0.9436968 , 0.0563032 ]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6VZsSGfeYM0y",
        "outputId": "f628254e-848e-4831-cf0b-ec2eb629ab2a"
      },
      "source": [
        "#Calculating f1 score\r\n",
        "# if prediction is greater than or equal to 0.3 than 1 else 0\r\n",
        "# Where 0 is for positive sentiment tweets and 1 for negative sentiment tweets\r\n",
        "xgb=xgb[:,1]>=0.3\r\n",
        "\r\n",
        "# converting the results to integer type\r\n",
        "xgb_int=xgb.astype(np.int)\r\n",
        "\r\n",
        "# calculating f1 score\r\n",
        "xgb_bow=f1_score(y_valid_bow,xgb_int)\r\n",
        "\r\n",
        "xgb_bow"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5712012728719172"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ACRO7tIJYf_1",
        "outputId": "bb5c1012-bdf0-47a4-b224-9ee7de969916"
      },
      "source": [
        "#Using TF-IDF features\r\n",
        "model_tfidf=XGBClassifier(random_state=29,learning_rate=0.7)\r\n",
        "model_tfidf.fit(x_train_tfidf, y_train_tfidf)\r\n",
        "\r\n",
        "# The first part of the list is predicting probabilities for label:0 \r\n",
        "# and the second part of the list is predicting probabilities for label:1\r\n",
        "xgb_tfidf=model_tfidf.predict_proba(x_valid_tfidf)\r\n",
        "\r\n",
        "xgb_tfidf"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.9905174 , 0.00948263],\n",
              "       [0.9902541 , 0.00974591],\n",
              "       [0.95791286, 0.04208714],\n",
              "       ...,\n",
              "       [0.9883729 , 0.0116271 ],\n",
              "       [0.9878232 , 0.0121768 ],\n",
              "       [0.9807036 , 0.01929643]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4F3e7dXxY9p1",
        "outputId": "9c77e29f-9ecb-41ae-9750-ea573ee193a8"
      },
      "source": [
        "#Calculating f1 score\r\n",
        "# if prediction is greater than or equal to 0.3 than 1 else 0\r\n",
        "# Where 0 is for positive sentiment tweets and 1 for negative sentiment tweets\r\n",
        "xgb_tfidf=xgb_tfidf[:,1]>=0.3\r\n",
        "\r\n",
        "# converting the results to integer type\r\n",
        "xgb_int_tfidf=xgb_tfidf.astype(np.int)\r\n",
        "\r\n",
        "# calculating f1 score\r\n",
        "score=f1_score(y_valid_tfidf,xgb_int_tfidf)\r\n",
        "\r\n",
        "score"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5657051282051281"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o5OkuvUDZ29U",
        "outputId": "6ae6ed96-34aa-4eaa-e8ed-afa9a3d1b9ce"
      },
      "source": [
        "#Applying Decision Tree model\r\n",
        "dct = DecisionTreeClassifier(criterion='entropy', random_state=1)\r\n",
        "dct.fit(x_train_bow,y_train_bow)"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='entropy',\n",
              "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                       random_state=1, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V5x80qFRaP2C",
        "outputId": "1162b043-19d8-4b12-9ebd-50236def14db"
      },
      "source": [
        "#Prediction using Decision trees\r\n",
        "dct_bow = dct.predict_proba(x_valid_bow)\r\n",
        "\r\n",
        "dct_bow"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 0.],\n",
              "       [1., 0.],\n",
              "       [1., 0.],\n",
              "       ...,\n",
              "       [1., 0.],\n",
              "       [1., 0.],\n",
              "       [1., 0.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zdfchkDlaggB",
        "outputId": "cdd1a1e5-d453-4236-b271-27b4dfdf3215"
      },
      "source": [
        "# if prediction is greater than or equal to 0.3 than 1 else 0\r\n",
        "# Where 0 is for positive sentiment tweets and 1 for negative sentiment tweets\r\n",
        "dct_bow=dct_bow[:,1]>=0.3\r\n",
        "\r\n",
        "# converting the results to integer type\r\n",
        "dct_int_bow=dct_bow.astype(np.int)\r\n",
        "\r\n",
        "# calculating f1 score\r\n",
        "dct_score_bow=f1_score(y_valid_bow,dct_int_bow)\r\n",
        "\r\n",
        "dct_score_bow"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5141776937618148"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sVlJT6hfap8Y",
        "outputId": "720e589d-d071-4e4b-8cea-b18f09b15535"
      },
      "source": [
        "#Using TF-IDF features\r\n",
        "dct.fit(x_train_tfidf,y_train_tfidf)"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='entropy',\n",
              "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                       random_state=1, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5F9IBYDZaybQ",
        "outputId": "b2ba6297-7ab1-4288-a238-aa4d21c7c931"
      },
      "source": [
        "#Prediction using Decision Tree(TF-IDF features)\r\n",
        "dct_tfidf = dct.predict_proba(x_valid_tfidf)\r\n",
        "\r\n",
        "dct_tfidf"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 0.],\n",
              "       [1., 0.],\n",
              "       [1., 0.],\n",
              "       ...,\n",
              "       [1., 0.],\n",
              "       [1., 0.],\n",
              "       [1., 0.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OXIj7F9ebDx2",
        "outputId": "abb09136-4133-4488-ce9b-b57812b84084"
      },
      "source": [
        "#Calculating f1 score\r\n",
        "# if prediction is greater than or equal to 0.3 than 1 else 0\r\n",
        "# Where 0 is for positive sentiment tweets and 1 for negative sentiment tweets\r\n",
        "dct_tfidf=dct_tfidf[:,1]>=0.3\r\n",
        "\r\n",
        "# converting the results to integer type\r\n",
        "dct_int_tfidf=dct_tfidf.astype(np.int)\r\n",
        "\r\n",
        "# calculating f1 score\r\n",
        "dct_score_tfidf=f1_score(y_valid_tfidf,dct_int_tfidf)\r\n",
        "\r\n",
        "dct_score_tfidf"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5498821681068342"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0Rn2P1mdbbSo"
      },
      "source": [
        "**Model Comparision**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        },
        "id": "VIJIBwflbffZ",
        "outputId": "03781b3f-3c17-400b-b1d9-074806d5f88e"
      },
      "source": [
        "Algo=['LogisticRegression(Bag-of-Words)','XGBoost(Bag-of-Words)','DecisionTree(Bag-of-Words)','LogisticRegression(TF-IDF)','XGBoost(TF-IDF)','DecisionTree(TF-IDF)']\r\n",
        "score = [log_bow,xgb_bow,dct_score_bow,log_tfidf,score,dct_score_tfidf]\r\n",
        "\r\n",
        "compare=pd.DataFrame({'Model':Algo,'F1_Score':score},index=[i for i in range(1,7)])\r\n",
        "compare.T"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Model</th>\n",
              "      <td>LogisticRegression(Bag-of-Words)</td>\n",
              "      <td>XGBoost(Bag-of-Words)</td>\n",
              "      <td>DecisionTree(Bag-of-Words)</td>\n",
              "      <td>LogisticRegression(TF-IDF)</td>\n",
              "      <td>XGBoost(TF-IDF)</td>\n",
              "      <td>DecisionTree(TF-IDF)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>F1_Score</th>\n",
              "      <td>0.572135</td>\n",
              "      <td>0.571201</td>\n",
              "      <td>0.514178</td>\n",
              "      <td>0.586207</td>\n",
              "      <td>0.565705</td>\n",
              "      <td>0.549882</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                         1  ...                     6\n",
              "Model     LogisticRegression(Bag-of-Words)  ...  DecisionTree(TF-IDF)\n",
              "F1_Score                          0.572135  ...              0.549882\n",
              "\n",
              "[2 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "VgB29VVhbzwM",
        "outputId": "8df9d3d4-3fcf-4286-e012-c141147040de"
      },
      "source": [
        "#Plotting the f1 score for different models\r\n",
        "plt.figure(figsize=(18,5))\r\n",
        "\r\n",
        "sns.pointplot(x='Model',y='F1_Score',data=compare)\r\n",
        "\r\n",
        "plt.title('Model Vs Score')\r\n",
        "plt.xlabel('MODEL')\r\n",
        "plt.ylabel('SCORE')\r\n",
        "\r\n",
        "plt.show()"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P4DVJqOocCv3"
      },
      "source": [
        "From the above comparsion, it is clear that logistic regression is the best model using the TF-IDF features."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "T8ho2vTYc9GF",
        "outputId": "5197a62d-384f-458d-b0b8-7dd4163f90c7"
      },
      "source": [
        "test_tfidf = tfidf_matrix[31962:]\r\n",
        "\r\n",
        "test_pred = Log_Reg.predict_proba(test_tfidf)\r\n",
        "\r\n",
        "test_pred_int = test_pred[:,1] >= 0.3\r\n",
        "\r\n",
        "test_pred_int = test_pred_int.astype(np.int)\r\n",
        "\r\n",
        "test['label'] = test_pred_int\r\n",
        "\r\n",
        "test_pred_ = test[['id','label']]\r\n",
        "\r\n",
        "test_pred_"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>31963</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>31964</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>31965</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>31966</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>31967</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17192</th>\n",
              "      <td>49155</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17193</th>\n",
              "      <td>49156</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17194</th>\n",
              "      <td>49157</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17195</th>\n",
              "      <td>49158</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17196</th>\n",
              "      <td>49159</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>17197 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          id  label\n",
              "0      31963      0\n",
              "1      31964      0\n",
              "2      31965      0\n",
              "3      31966      0\n",
              "4      31967      0\n",
              "...      ...    ...\n",
              "17192  49155      1\n",
              "17193  49156      0\n",
              "17194  49157      0\n",
              "17195  49158      0\n",
              "17196  49159      0\n",
              "\n",
              "[17197 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OsUSN3Gqdmdy"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}